SnapLogic MCP Support
Introduction Since the inception of the Model Context Protocol (MCP), we've been envisioning and designing how it can be integrated into the SnapLogic platform. We've recently received a significant number of inquiries about MCP, and we're excited to share our progress, the features we'll be supporting, our release timeline, and how you can get started creating MCP servers and clients within SnapLogic. If you're interested, we encourage you to reach out! Understanding the MCP Protocol The MCP protocol allows tools, data resources, and prompts to be published by an MCP server in a way that Large Language Models (LLMs) can understand. This empowers LLMs to autonomously interact with these resources via an MCP client, expanding their capabilities to perform actions, retrieve information, and execute complex workflows. MCP Protocol primarily supports: Tools: Functions an LLM can invoke (e.g., data lookups, operational tasks). Resources: File-like data an LLM can read (e.g., API responses, file contents). Prompts: Pre-written templates to guide LLM interaction with the server. Sampling (not widely used): Allows client-hosted LLMs to be used by remote MCP servers. An MCP client can, therefore, request to list available tools, call specific tools, list resources, or read resource content from a server. Transport and Authentication MCP protocol offers flexible transport options, including STDIO or HTTP (SSE or Streamable-HTTP) for local deployments, and HTTP (SSE or Streamable-HTTP) for remote deployments. While the protocol proposes OAuth 2.1 for authentication, an MCP server can also use custom headers for security. Release Timeline We're excited to bring MCP support to SnapLogic with two key releases: August Release: MCP Client Support We'll be releasing two new snaps: the MCP Function Generator Snap and the MCP Invoke Snap. These will be available in the AgentCreator Experimental (Beta) Snap Pack. With these Snaps, your SnapLogic agent can access the services and resources available on the public MCP server. Late Q3 Release: MCP Server Support Our initial MCP server support will focus on tool operations, including the ability to list tools and call tools. For authentication, it will support custom header-based authentication. Users will be able to leverage the MCP Server functionality by subscribing to this feature. If you're eager to be among the first to test these new capabilities and provide feedback, please reach out to the Project Manager Team, at pm-team@snaplogic.com. We're looking forward to seeing what you build with SnapLogic MCP. SnapLogic MCP Client MCP Clients in SnapLogic enable users to connect to MCP servers as part of their Agent. An example can be connecting to the Firecrawl MCP server for a data scraping Agent, or other use cases that can leverage the created MCP servers. The MCP Client support in SnapLogic consists of two Snaps, the MCP Function Generator Snap and the MCP Invoke Snap. From a high-level perspective, the MCP Function generator Snap allows users to list available tools from an MCP server, and the MCP Invoke Snap allows users to perform operations such as call tools, list resources, and read resources from an MCP server. Let’s dive into the individual pieces. MCP SSE Account To connect to an MCP Server, we will need an account to specify the URI of the server to connect to. Properties URI The URI of the server to connect to. Don’t need to include the /sse path Additional headers Additional HTTP headers to be sent to the server Timeout The timeout value in seconds, if the result is not returned within the timeout, the Snap will return an error. MCP Function Generator Snap The MCP Function Generator Snap enables users to retrieve the list of tools as SnapLogic function definitions to be used in a Tool Calling Snap. Properties Account An MCP SSE account is required to connect to an MCP Server. Expose Tools List all available tools from an MCP server as SnapLogic function definitions Expose Resources Add list_resources, read_resource as SnapLogic function definitions to allow LLMs to use resources/read and resources/list (MCP Resources). Definitions for list resource and read resource [ { "sl_type": "function", "name": "list_resources", "description": "This function lists all available resources on the MCP server. Return a list of resources with their URIs.", "strict": false, "sl_tool_metadata": { "operation": "resources/list" } }, { "sl_type": "function", "name": "read_resource", "description": "This function returns the content of the resource from the MCP server given the URI of the resource.", "strict": false, "sl_tool_metadata": { "operation": "resources/read" }, "parameters": [ { "name": "uri", "type": "STRING", "description": "Unique identifier for the resource", "required": true } ] } ] MCP Invoke Snap The MCP Invoke Snap enables users to perform operations such as tools/call, resources/list, and resources/read to an MCP server. Properties Account An account is required to use the MCP Invoke Snap Operation The operation to perform on the MCP server. The operation must be one of tools/call, resources/list, or resources/read Tool Name The name of the tool to call. Only enabled and required when the operation is tools/call Parameters The parameters to be added to the operation. Only enabled for resources/read and tools/call. Required for resources/read, and optional for tools/call, based on the tool. MCP Agents in pipeline action MCP Agent Driver pipeline An MCP Agent Driver pipeline is like any other MCP Agent pipeline; we’ll need to provide the system prompt, user prompt, and run it with the PipeLoop Snap. MCP Agent Worker pipeline Here’s an example of an MCP Agent with a single MCP Server connection. The MCP Agent Worker is connected to one MCP Server. MCP Client Snaps can be used together with AgentCreator Snaps, such as the Multi-Pipeline Function Generator and Pipeline Execute Snap, as SnapLogic Functions, tools. This allows users to use tools provided by MCP servers and internal tools, without sacrificing safety and freedom when building an Agent. Agent Worker with MCP Client Snaps SnapLogic MCP Server In SnapLogic, an MCP Server allows you to expose SnapLogic pipelines as dynamic tools that can be discovered and invoked by language models or external systems. By registering an MCP Server, you effectively provide a API that language models and other clients can use to perform operations such as data retrieval, transformation, enrichment, or automation, all orchestrated through SnapLogic pipelines. For the initial phase, we'll support connections to the server via HTTP + SSE. Core Capabilities The MCP Server provides two core capabilities. The first is listing tools, which returns structured metadata that describes the available pipelines. This metadata includes the tool name, a description, the input schema in JSON Schema format, and any additional relevant information. This allows clients to dynamically discover which operations are available for invocation. The second capability is calling tools, where a specific pipeline is executed as a tool using structured input parameters, and the output is returned. Both of these operations—tool listing and tool calling—are exposed through standard JSON-RPC methods, specifically tools/list and tools/call, accessible over HTTP. Prerequisite You'll need to prepare your tool pipelines in advance. During the server creation process, these can be added and exposed as tools for external LLMs to use. MCP Server Pipeline Components A typical MCP server pipeline consists of four Snaps, each with a dedicated role: 1. Router What it does: Routes incoming JSON requests—which differ from direct JSON-RPC requests sent by an MCP client—to either the list tools branch or the call tool branch. How: Examines the request payload (typically the method field) to determine which action to perform. 2. Multi-Pipeline Function Generator (Listing Tools) What it does: Converts a list of pipeline references into tool metadata. This is where you define the pipelines you want the server to expose as tools. Output: For each pipeline, generates: Tool name Description Parameters (as JSON Schema) Other metadata Purpose: Allows clients (e.g., an LLM) to query what tools are available without prior knowledge. 3. Pipeline Execute (Calling Tools) What it does: Dynamically invokes the selected SnapLogic pipeline and returns structured outputs. How: Accepts parameters encoded in the request body, maps them to the pipeline’s expected inputs, and executes the pipeline. Purpose: Provides flexible runtime execution of tools based on user or model requests. 4. Union What it does: Merges the result streams from both branches (list and call) into a single output stream for consistent response formatting. Request Flows Below are example flows showing how requests are processed: 🟢 tools/list Client sends a JSON-RPC request with method = "tools/list". Router directs the request to the Multi-Pipeline Function Generator. Tool metadata is generated and returned in the response. Union Snap merges and outputs the content. ✅ Result: The client receives a JSON list describing all available tools. �� tools/call Client sends a JSON-RPC request with method = "tools/call" and the tool name + parameters. Router sends this to the Pipeline Execute Snap. The selected pipeline is invoked with the given parameters. Output is collected and merged via Union. ✅ Result: The client gets the execution result of the selected tool. Registering an MCP Server Once your MCP server pipeline is created: Create a Trigger Task and Register as an MCP Server Navigate to the Designer > Create Trigger Task Choose a Groundplex. (Note: This capability currently requires a Groundplex, not a Cloudplex.) Select your MCP pipeline. Click Register as MCP server Configure node and authentication. Find your MCP Server URL Navigate to the Manager > Tasks The Task Details page exposes a unique HTTP endpoint. This endpoint is treated as your MCP Server URL. After registration, clients such as AI models or orchestration engines can interact with the MCP Server by calling the /tools/list endpoint to discover the available tools, and the /tools/call endpoint to invoke a specific tool using a structured JSON payload. Connect to a SnapLogic MCP Server from a Client After the MCP server is successfully published, using the SnapLogic MCP server is no different from using other MCP servers running in SSE mode. It can be connected to by any MCP client that supports SSE mode; all you need is the MCP Server URL (and the Bearer Token if authentication is enabled during server registration). Configuration First, you need to add your MCP server in the settings of the MCP client. Taking Claude Desktop as an example, you'll need to modify your Claude Desktop configuration file. The configuration file is typically located at: macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Add your remote MCP server configuration to the mcpServers section: { "mcpServers": { "SL_MCP_server": { "command": "npx", "args": [ "mcp-remote", "http://devhost9000.example.com:9000/mcp/6873ff343a91cab6b00014a5/sse", "--header", "Authorization: Bearer your_token_here" ] } } } Key Components Server Name: SL_MCP_server - A unique identifier for your MCP server Command: npx - Uses the Node.js package runner to execute the mcp-remote package URL: The SSE endpoint URL of your remote MCP server (note the /sse suffix) Authentication: Use the --header flag to include authorization tokens if the server enabled authentication Requirements Ensure you have Node.js installed on your system, as the configuration uses npx to run the mcp-remote package. Replace the example URL and authorization token with your actual server details before saving the configuration. After updating the configuration file, restart Claude Desktop for the changes to take effect. To conclude, the MCP Server in SnapLogic is a framework that allows you to expose pipelines as dynamic tools accessible through a single HTTP endpoint. This capability is designed for integration with language models and external systems that need to discover and invoke SnapLogic workflows at runtime. MCP Servers make it possible to build flexible, composable APIs that return structured results, supporting use cases such as conversational AI, automated data orchestration, and intelligent application workflows. Conclusion SnapLogic's integration of the MCP protocol marks a significant leap forward in empowering LLMs to dynamically discover and invoke SnapLogic pipelines as sophisticated tools, transforming how you build conversational AI, automate complex data orchestrations, and create truly intelligent applications. We're excited to see the innovative solutions you'll develop with these powerful new capabilities.
OpenAI Responses API
Introduction OpenAI announced the Responses API, their most advanced and versatile interface for building intelligent AI applications. Supporting both text and image inputs with rich text outputs, this API enables dynamic, stateful conversations that remember and build on previous interactions, making AI experiences more natural and context-aware. It also unlocks powerful capabilities through built-in tools such as web search, file search, code interpreter, and more, while enabling seamless integration with external systems via function calling. Its event-driven design delivers clear, structured updates at every step, making it easier than ever to create sophisticated, multi-step AI workflows. Key features include: Stateful conversations via the previous response ID Built-in tools like web search, file search, code interpreter, MCP, and others Access to advanced models available exclusively, such as o1-pro Enhanced support for reasoning models with reasoning summaries and efficient context management through previous response ID or encrypted reasoning items Clear, event-based outputs that simplify integration and control While the Chat Completions API remains fully supported and widely used, OpenAI plans to retire the Assistants API in the first half of 2026. To support the adoption of the Responses API, two new Snaps have been introduced: OpenAI Chat Completions ⇒ OpenAI Responses API Generation OpenAI Tool Calling ⇒ OpenAI Responses API Tool Calling Both Snaps are fully compatible with existing upstream and downstream utility Snaps, including the OpenAI Prompt Generator, OpenAI Multimodal Content Generator, all Function Generators (Multi-Pipeline, OpenAPI, and APIM), the Function Result Generator, and the Message Appender. This allows existing pipelines and familiar development patterns to be reused while gaining access to the advanced features of the Responses API. OpenAI Responses API Generation The OpenAI Responses API Generation Snap is designed to support OpenAI’s newest Responses API, enabling more structured, stateful, and tool-augmented interactions. While it builds upon the familiar interface of the Chat Completions Snap, several new properties and behavioral updates have been introduced to align with the Responses API’s capabilities. New properties Message: The input sent to the LLM. This field replaces the previous Use message payload, Message payload, and Prompt properties in the OpenAI Chat Completions Snap, consolidating them into a single input. It removes ambiguity between "prompt" as raw text and as a template, and supports both string and list formats. Previous response ID: The unique ID of the previous response to the model. Use this to create multi-turn conversations. Model parameters Reasoning summary: For reasoning models, provides a summary of the model’s reasoning process, aiding in debugging and understanding the model's reasoning process. The property can be none, auto, or detailed. Advanced prompt configurations Instructions: Applied only to the current response, making them useful for dynamically swapping instructions between turns. To persist instructions across turns when using previous_response_id, the developer message in the OpenAI Prompt Generator Snap should be used. Advanced response configurations Truncation: Defines how to handle input that exceeds the model’s context window. auto allows the model to truncate the middle of the conversation to fit, while disabled (default) causes the request to fail with a 400 error if the context limit is exceeded. Include reasoning encrypted content: Includes an encrypted version of reasoning tokens in the output, allowing reasoning items to persist when the store is disabled. Built-in tools Web search: Enables the model to access up-to-date information from the internet to answer queries beyond its training data. Web search type Search context size User location: an approximate user location including city, region, country, and timezone to deliver more relevant search results. File search: Allows the model to retrieve information from documents or files. Vector store IDs Maximum number of results Include search results: Determines whether raw search results are included in the response for transparency or debugging. Ranker Score threshold Filters: Additional metadata-based filters to refine search results. For more details on using filters, see Metadata Filtering. Advanced tool configuration Tool choice: A new option, SPECIFY A BUILT-IN TOOL, allows specifying that the model should use a built-in tool to generate a response. Note that the OpenAI Responses API Generation Snap does not support the response count or stop sequences properties, as these are not available in the Responses API. Additionally, the message user name, which may be specified in the Prompt Generator Snap, is not supported and will be ignored if included. Model response of Chat Completions vs Responses API Chat Completions API Responses API The Responses API introduces an event-driven output structure that significantly enhances how developers build and manage AI-powered applications compared to the traditional Chat Completions API. While the Chat Completions API returns a single, plain-text response within the choices array, the Responses API provides an output array containing a sequence of semantic event items—such as reasoning, message, function_call, web_search_call, and more—that clearly delineate each step in the model's reasoning and actions. This structured approach allows developers to easily track and interpret the model's behavior, facilitating more robust error handling and smoother integration with external tools. Moreover, the response from the Responses API includes the model parameters settings, providing additional context for developers. Pipeline examples Built-in tool: web search This example demonstrates how to use the built-in web search tool. In this pipeline, the user’s location is specified to ensure the web search targets relevant geographic results. System prompt: You are a friendly and helpful assistant. Please use your judge to decide whether to use the appropriate tools or not to answer questions from the user. Prompt: Can you recommend 2 good sushi restaurants near me? Output: As a result, the output contains both a web search call and a message. The model uses the web search to find and provide recommendations based on current data, tailored to the specified location. Built-in tool: File search This example demonstrates how the built-in file search tool enables the model to retrieve information from documents stored in a vector store during response generation. In this case, the file wildfire_stats.pdf has been uploaded. You can create and manage vector stores through the Vector Store management page. Prompt: What is the number of Federal wildfires in 2018 Output: The output array contains a file_search_call event, which includes search results in its results field. These results provide matched text, metadata, and relevance scores from the vector store. This is followed by a message event, where the model uses the retrieved information to generate a grounded response. The presence of detailed results in the file_search_call is enabled by selecting the Include file search results option. OpenAI Responses API Tool Calling The OpenAI Responses API Tool Calling Snap is designed to support function calling using OpenAI’s Responses API. It works similarly to the OpenAI Tool Calling Snap (which uses the Chat Completions API), but is adapted to the event-driven response structure of the Responses API and supports stateful interactions via the previous response ID. While it shares much of its configuration with the Responses API Generation Snap, it is purpose-built for workflows involving function calls. Existing LLM agent pipeline patterns and utility Snaps—such as the Function Generator and Function Result Generator—can continue to be used with this Snap, just as with the original OpenAI Tool Calling Snap. The primary difference lies in adapting the Snap configuration to accommodate the Responses API’s event-driven output, particularly the structured function_call event item in the output array. The Responses API Tool Calling Snap provides two output views, similar to the OpenAI Tool Calling Snap, with enhancements to simplify building agent pipelines and support stateful interactions using the previous response ID: Model response view: The complete API response, including extra fields: messages: an empty list if store is enabled, or the full message history—including messages payload and model response—if disabled (similar to the OpenAI Tool Calling Snap). When using stateful workflows, message history isn’t needed because the previous response ID is used to maintain context. has_tool_call: a boolean indicating whether the response includes a tool call. Since the Responses API no longer includes the finish_reason: "tool_calls" field, this new field makes it easier to create stop conditions in the pipeloop Snap within the agent driver pipeline. Tool call view: Displays the list of function calls made by the model during the interaction. Tool Call View of Chat Completions vs Responses API Uses id as the function call identifier when sending back the function result. Tool call properties (name, arguments) are nested inside the function field. Each tool call includes: • id: the unique event ID • call_id: used to reference the function call when returning the result The tool call structure is flat — name and arguments are top-level fields. Building LLM Agent Pipelines To build LLM agent pipelines with the OpenAI Responses API Tool Calling Snap, you can reuse the same agent pipeline pattern described in Introducing Tool Calling Snaps and LLM Agent Pipelines. Only minor configuration changes are needed to support the Responses API. Agent Driver Pipeline The primary change is in the PipeLoop Snap configuration, where the stop condition should now check the has_tool_call field, since the Responses API no longer includes the finish_reason:"tool_calls". Agent Worker Pipeline Fields mapping A Mapper Snap is used to prepare the related fields for the OpenAI Responses API Tool Calling Snap. OpenAI Responses API Tool Calling The key changes are in this Snap’s configuration to support the Responses API’s stateful interactions. There are two supported approaches: Option 1: Use Store (Recommended) Leverages the built-in state management of the Responses API. Enable Store Use Previous Response ID Send only the function call results as the input messages for the next round. (messages field in the Snap’s output will be an empty array, so you can still use it in the Message Appender Snap to collect tool results.) Option 2: Maintain Conversation History in Pipeline Similar to the approach used in the Chat Completions API. Disable Store Include the full message history in the input (messages field in the Snap’s output contains message history) (Optional) Enable Include Reasoning Encrypted Content (for reasoning models) to preserve reasoning context efficiently OpenAI Function Result Generator As explained in Tool Call View of Chat Completions vs Responses API section, the Responses API includes both an id and a call_id. You must use the call_id to construct the function call result when sending it back to the model. Conclusion The OpenAI Responses API makes AI workflows smarter and more adaptable, with stateful interactions and built-in tools. SnapLogic’s OpenAI Responses API Generation and Tool Calling Snaps bring these capabilities directly into your pipelines, letting you take advantage of advanced features like built-in tools and event-based outputs with only minimal adjustments. By integrating these Snaps, you can seamlessly enhance your workflows and fully unlock the potential of the Responses API.OpenAI Responses API
Introduction OpenAI announced the Responses API, their most advanced and versatile interface for building intelligent AI applications. Supporting both text and image inputs with rich text outputs, this API enables dynamic, stateful conversations that remember and build on previous interactions, making AI experiences more natural and context-aware. It also unlocks powerful capabilities through built-in tools such as web search, file search, code interpreter, and more, while enabling seamless integration with external systems via function calling. Its event-driven design delivers clear, structured updates at every step, making it easier than ever to create sophisticated, multi-step AI workflows. Key features include: Stateful conversations via the previous response ID Built-in tools like web search, file search, code interpreter, MCP, and others Access to advanced models available exclusively, such as o1-pro Enhanced support for reasoning models with reasoning summaries and efficient context management through previous response ID or encrypted reasoning items Clear, event-based outputs that simplify integration and control While the Chat Completions API remains fully supported and widely used, OpenAI plans to retire the Assistants API in the first half of 2026. To support the adoption of the Responses API, two new Snaps have been introduced: OpenAI Chat Completions ⇒ OpenAI Responses API Generation OpenAI Tool Calling ⇒ OpenAI Responses API Tool Calling Both Snaps are fully compatible with existing upstream and downstream utility Snaps, including the OpenAI Prompt Generator, OpenAI Multimodal Content Generator, all Function Generators (Multi-Pipeline, OpenAPI, and APIM), the Function Result Generator, and the Message Appender. This allows existing pipelines and familiar development patterns to be reused while gaining access to the advanced features of the Responses API. OpenAI Responses API Generation The OpenAI Responses API Generation Snap is designed to support OpenAI’s newest Responses API, enabling more structured, stateful, and tool-augmented interactions. While it builds upon the familiar interface of the Chat Completions Snap, several new properties and behavioral updates have been introduced to align with the Responses API’s capabilities. New properties Message: The input sent to the LLM. This field replaces the previous Use message payload, Message payload, and Prompt properties in the OpenAI Chat Completions Snap, consolidating them into a single input. It removes ambiguity between "prompt" as raw text and as a template, and supports both string and list formats. Previous response ID: The unique ID of the previous response to the model. Use this to create multi-turn conversations. Model parameters Reasoning summary: For reasoning models, provides a summary of the model’s reasoning process, aiding in debugging and understanding the model's reasoning process. The property can be none, auto, or detailed. Advanced prompt configurations Instructions: Applied only to the current response, making them useful for dynamically swapping instructions between turns. To persist instructions across turns when using previous_response_id, the developer message in the OpenAI Prompt Generator Snap should be used. Advanced response configurations Truncation: Defines how to handle input that exceeds the model’s context window. auto allows the model to truncate the middle of the conversation to fit, while disabled (default) causes the request to fail with a 400 error if the context limit is exceeded. Include reasoning encrypted content: Includes an encrypted version of reasoning tokens in the output, allowing reasoning items to persist when the store is disabled. Built-in tools Web search: Enables the model to access up-to-date information from the internet to answer queries beyond its training data. Web search type Search context size User location: an approximate user location including city, region, country, and timezone to deliver more relevant search results. File search: Allows the model to retrieve information from documents or files. Vector store IDs Maximum number of results Include search results: Determines whether raw search results are included in the response for transparency or debugging. Ranker Score threshold Filters: Additional metadata-based filters to refine search results. For more details on using filters, see Metadata Filtering. Advanced tool configuration Tool choice: A new option, SPECIFY A BUILT-IN TOOL, allows specifying that the model should use a built-in tool to generate a response. Note that the OpenAI Responses API Generation Snap does not support the response count or stop sequences properties, as these are not available in the Responses API. Additionally, the message user name, which may be specified in the Prompt Generator Snap, is not supported and will be ignored if included. Model response of Chat Completions vs Responses API Chat Completions API Responses API The Responses API introduces an event-driven output structure that significantly enhances how developers build and manage AI-powered applications compared to the traditional Chat Completions API. While the Chat Completions API returns a single, plain-text response within the choices array, the Responses API provides an output array containing a sequence of semantic event items—such as reasoning, message, function_call, web_search_call, and more—that clearly delineate each step in the model's reasoning and actions. This structured approach allows developers to easily track and interpret the model's behavior, facilitating more robust error handling and smoother integration with external tools. Moreover, the response from the Responses API includes the model parameters settings, providing additional context for developers. Pipeline examples Built-in tool: web search This example demonstrates how to use the built-in web search tool. In this pipeline, the user’s location is specified to ensure the web search targets relevant geographic results. System prompt: You are a friendly and helpful assistant. Please use your judge to decide whether to use the appropriate tools or not to answer questions from the user. Prompt: Can you recommend 2 good sushi restaurants near me? Output: As a result, the output contains both a web search call and a message. The model uses the web search to find and provide recommendations based on current data, tailored to the specified location. Built-in tool: File search This example demonstrates how the built-in file search tool enables the model to retrieve information from documents stored in a vector store during response generation. In this case, the file wildfire_stats.pdf has been uploaded. You can create and manage vector stores through the Vector Store management page. Prompt: What is the number of Federal wildfires in 2018 Output: The output array contains a file_search_call event, which includes search results in its results field. These results provide matched text, metadata, and relevance scores from the vector store. This is followed by a message event, where the model uses the retrieved information to generate a grounded response. The presence of detailed results in the file_search_call is enabled by selecting the Include file search results option. OpenAI Responses API Tool Calling The OpenAI Responses API Tool Calling Snap is designed to support function calling using OpenAI’s Responses API. It works similarly to the OpenAI Tool Calling Snap (which uses the Chat Completions API), but is adapted to the event-driven response structure of the Responses API and supports stateful interactions via the previous response ID. While it shares much of its configuration with the Responses API Generation Snap, it is purpose-built for workflows involving function calls. Existing LLM agent pipeline patterns and utility Snaps—such as the Function Generator and Function Result Generator—can continue to be used with this Snap, just as with the original OpenAI Tool Calling Snap. The primary difference lies in adapting the Snap configuration to accommodate the Responses API’s event-driven output, particularly the structured function_call event item in the output array. The Responses API Tool Calling Snap provides two output views, similar to the OpenAI Tool Calling Snap, with enhancements to simplify building agent pipelines and support stateful interactions using the previous response ID: Model response view: The complete API response, including extra fields: messages: an empty list if store is enabled, or the full message history—including messages payload and model response—if disabled (similar to the OpenAI Tool Calling Snap). When using stateful workflows, message history isn’t needed because the previous response ID is used to maintain context. has_tool_call: a boolean indicating whether the response includes a tool call. Since the Responses API no longer includes the finish_reason: "tool_calls" field, this new field makes it easier to create stop conditions in the pipeloop Snap within the agent driver pipeline. Tool call view: Displays the list of function calls made by the model during the interaction. Tool Call View of Chat Completions vs Responses API Uses id as the function call identifier when sending back the function result. Tool call properties (name, arguments) are nested inside the function field. Each tool call includes: • id: the unique event ID • call_id: used to reference the function call when returning the result The tool call structure is flat — name and arguments are top-level fields. Building LLM Agent Pipelines To build LLM agent pipelines with the OpenAI Responses API Tool Calling Snap, you can reuse the same agent pipeline pattern described in Introducing Tool Calling Snaps and LLM Agent Pipelines. Only minor configuration changes are needed to support the Responses API. Agent Driver Pipeline The primary change is in the PipeLoop Snap configuration, where the stop condition should now check the has_tool_call field, since the Responses API no longer includes the finish_reason:"tool_calls". Agent Worker Pipeline Fields mapping A Mapper Snap is used to prepare the related fields for the OpenAI Responses API Tool Calling Snap. OpenAI Responses API Tool Calling The key changes are in this Snap’s configuration to support the Responses API’s stateful interactions. There are two supported approaches: Option 1: Use Store (Recommended) Leverages the built-in state management of the Responses API. Enable Store Use Previous Response ID Send only the function call results as the input messages for the next round. (messages field in the Snap’s output will be an empty array, so you can still use it in the Message Appender Snap to collect tool results.) Option 2: Maintain Conversation History in Pipeline Similar to the approach used in the Chat Completions API. Disable Store Include the full message history in the input (messages field in the Snap’s output contains message history) (Optional) Enable Include Reasoning Encrypted Content (for reasoning models) to preserve reasoning context efficiently OpenAI Function Result Generator As explained in Tool Call View of Chat Completions vs Responses API section, the Responses API includes both an id and a call_id. You must use the call_id to construct the function call result when sending it back to the model. Conclusion The OpenAI Responses API makes AI workflows smarter and more adaptable, with stateful interactions and built-in tools. SnapLogic’s OpenAI Responses API Generation and Tool Calling Snaps bring these capabilities directly into your pipelines, letting you take advantage of advanced features like built-in tools and event-based outputs with only minimal adjustments. By integrating these Snaps, you can seamlessly enhance your workflows and fully unlock the potential of the Responses API.Agentic Builders Webinar Series - Integrated agentic workflows, built live, every week
Register Here>> The Agentic Builders webinar series is your step-by-step guide to designing powerful, AI-powered workflows that transform how work gets done. Across five live sessions, SnapLogic experts will show you how to connect your data, automate complex tasks, and empower teams to put AI to work across departments including: sales, finance, customer success, learning services, and revenue operations. What you’ll take away: See agentic workflows built live, integrating data sources and tools you already use. Learn how to automate high-value, high-effort tasks across your organization. Discover best practices for connecting CRM, support, LMS, and financial systems. Walk away with actionable steps to design your first (or next) agentic workflow. Starts August 28th and runs through September 25th. Explore the series!More Than Just Fast: A Holistic Guide to High-Performance AI Agents
At SnapLogic, while building and refining an AI Agent for a large customer in the healthcare industry, we embarked on a journey of holistic performance optimization. We didn't just want to make it faster. We tried to make it better across the board. This journey taught us that significant gains are found by looking at the entire system, from the back-end data sources to the pixels on the user's screen. Here’s our playbook for building a truly high-performing AI agent, backed by real-world metrics. The Foundation: Data and Architecture Before you can tune an engine, you have to build it on a solid chassis. For an AI Agent, that chassis is its core architecture and its relationship with data. Choose the Right Brain for the Job: Not all LLMs are created equal. The "best" model depends entirely on the nature of the tasks your agent needs to perform. A simple agent with one or two tools has very different requirements from a complex agent that needs to reason, plan, and execute dynamic operations. Matching the model to the task complexity is key to balancing cost, speed, and capability. Task Complexity Model Type Characteristics & Best For Simple, Single-Tool Tasks Fast & Cost-Effective Goal: Executing a well-defined task with a limited toolset (e.g., simple data lookups, classification). These models are fast and cheap, perfect for high-volume, low-complexity actions. Multi-Tool Orchestration Balanced Goal: Reliably choosing the correct tool from several options and handling moderately complex user requests. These models offer a great blend of speed, cost, and improved instruction-following for a good user experience. Complex Reasoning & Dynamic Tasks High-Performance / Sophisticated Goal: Handling ambiguous requests that require multi-step reasoning, planning, and advanced tool use like dynamic SQL query generation. These are the most powerful (and expensive) models, essential for tasks where deep understanding and accuracy are critical. Deconstruct Complexity with a Multi-Agent Approach: A single, monolithic agent designed to do everything can become slow and unwieldy. A more advanced approach is to break down a highly complex agent into a team of smaller, specialized agents. This strategy offers two powerful benefits: It enables the use of faster, cheaper models. Each specialized agent has a narrower, more defined task, which often means you can use a less powerful (and faster) LLM for that specific job, reserving your most sophisticated model for the "manager" agent that orchestrates the others. It dramatically increases reusability. These smaller, function-specific agents and their underlying tools are modular. They can be easily repurposed and reused in the next AI Agent you build, accelerating future development cycles. Set the Stage for Success with Data: An AI Agent is only as good as the data it can access. We learned that optimizing data access is a critical first step. This involved: Implementing Dynamic Text-to-SQL: Instead of relying on rigid, pre-defined queries, we empowered the agent to build its own SQL queries dynamically from natural language. This flexibility required a deep initial investment in analyzing and understanding the critical columns and data formats our agent would need from sources like Snowflake. Generating Dedicated Database Views: To support the agent, we generated dedicated views on top of our source tables. This strategy serves two key purposes: it dramatically reduces query times by pre-joining and simplifying complex data, and it allows us to remove sensitive or unnecessary data from the source, ensuring the agent only has access to what it needs. Pre-loading the Schema for Agility: Making the database schema available to the agent is critical for accurate dynamic SQL generation. To optimize this, we pre-load the relevant schemas at startup. This simple step saves precious time on every single query the agent generates, contributing significantly to the overall responsiveness. The Engine: Tuning the Agent’s Logic and Retrieval Our Diagnostic Toolkit: Using AI to Analyze AI Before we could optimize the engine, we needed to know exactly where the friction was. Our diagnostic process followed a two-step approach: High-Level Analysis: We started in the SnapLogic Monitor, which provides a high-level, tabular view of all pipeline executions. This dashboard is the starting point for any performance investigation. As you can see below, it gives a list of all runs, their status, and their total duration. By clicking the Download table button, you can export this summary data as a CSV. This allows for a quick, high-level analysis to spot outliers and trends without immediately diving into verbose log files. AI-Powered Deep Dive: Once we identified a bottleneck from the dashboard—a pipeline that was taking longer than expected—we downloaded the detailed, verbose log files for those specific pipeline runs. We then fed these complex logs into an AI tool of our choice. This "AI analyzing AI" approach helped us instantly pinpoint key issues that would have taken hours to find manually. For example, this process uncovered an unnecessary error loop caused by duplicate JDBC driver versions, which significantly extended the execution time of our Snowflake Snaps. Fixing this single issue was a key factor in the 68% performance improvement we saw when querying our technical knowledge base. With a precise diagnosis in hand, we turned our attention to the agent's "thinking" process. This is where we saw some of our most dramatic performance gains. How We Achieved This: Crafting the Perfect Instructions (System Prompts): We transitioned from generic prompts to highly customized system prompts, optimized for both the specific task and the chosen LLM. A simpler model gets a simpler, more direct prompt, while a sophisticated model can be instructed to "think step-by-step" to improve its reasoning. A Simple Switch for Production Speed: One of the most impactful, low-effort optimizations came from how we use a key development tool: the Record Replay Snap. During the creation and testing of our agent's pipelines, this Snap is invaluable for capturing and replaying data, but it adds about 2.5 seconds of overhead to each execution. For a simple agent run involving a driver, a worker, and one tool, this adds up to 7.5 seconds of unnecessary latency in a production environment. Once our pipelines were successfully tested, we switched these Snaps to "Replay Only" mode. This simple change instantly removed the recording overhead, providing a significant speed boost across all agent interactions. Smarter, Faster Data Retrieval (RAG Optimization): For our Retrieval-Augmented Generation (RAG) tools, we focused on two key levers: Finding the Sweet Spot (k value): We tuned the k value—the number of documents retrieved for context. For our product information retrieval use case, adjusting this value was the key to our 63% speed improvement. It’s the art of getting just enough context for an accurate answer without creating unnecessary work for the LLM. Surgical Precision with Metadata: Instead of always performing a broad vector search, we enabled the agent to use metadata. If it knows a document's unique_ID, it can fetch that exact document. This is the difference between browsing a library and using a call number. It's swift and precise. Ensuring Consistency: We set the temperature to a low value during the data extraction and indexing process. This ensures that the data chunks are created consistently, leading to more reliable and repeatable search results. The Results: A Data-Driven Transformation Our optimization efforts led to significant, measurable improvements across several key use cases for the AI Agent. Use Case Before Optimization After Optimization Speed Improvement Querying Technical Knowledge Base 92 seconds 29 seconds ~68% Faster Processing Sales Order Data 32 seconds 10.7 seconds ~66% Faster RAG Retrieval 5.8 seconds 2.1 seconds ~63% Faster Production Optimization (Replay Only) 20 seconds 17.5 seconds ~12% Faster* (*This improvement came from switching development Snaps to a production-ready "Replay Only" mode, removing the latency inherent to the testing phase.) The Experience: Focusing on the User Ultimately, all the back-end optimization in the world is irrelevant if the user experience is poor. The final layer of our strategy was to focus on the front-end application. Engage, Don't Just Wait: A simple "running..." message can cause user anxiety and make any wait feel longer. Our next iteration will provide a real-time status of the agent's thinking process (e.g., "Querying product database...", "Synthesizing answer..."). This transparency keeps the user engaged and builds trust. Guide the User to Success: We learned that a blank text box can be intimidating. By providing predefined example prompts and clearly explaining the agent's capabilities, we guide the user toward successful interactions. Deliver a Clear Result: The final output must be easy to consume. We format our results cleanly, using tables, lists, and clear language to ensure the user can understand and act on the information instantly. By taking this holistic approach, we optimized the foundation, the engine, and the user experience to build an AI Agent that doesn't just feel fast. It feels intelligent, reliable, and genuinely helpful.
Building an AI Agent with SnapLogic AgentCreator using OpenAI and Microsoft Teams - Part 1
Building an AI Agent with SnapLogic AgentCreator using OpenAI and Microsoft Teams In this two-part blog series, I will cover how to create an AI Agent using SnapLogic's AgentCreator and integrate it with Microsoft Teams via Azure Bot services. The solution combines SnapLogic’s AgentCreator, OpenAI ( gpt 4.1 mini ) and Microsoft Teams ( to provide a familiar chat interface ). In the first part, we will cover building the agent with SnapLogic pipelines and the AgentCreator pattern. In the second part, I will explain the Azure setup and Teams integration, and highlight the business benefits of conversational automation. Designing the SnapLogic-Powered AI Agent The example I've decided to go with is a simple Weather Agent that provides a conversational interface for weather queries, accessible directly within Teams. This improves user experience by integrating information into the tools people already use and showcasing how SnapLogic’s AgentCreator can automate tasks through natural language. How does SnapLogic help? SnapLogic’s new AgentCreator framework allows us to build an AI-driven agent that uses LLM intelligence combined with SnapLogic pipelines to fetch real data. The Weather Agent understands a user’s question, decides if it needs to call a function ( tool ), performs that action via a SnapLogic pipeline, and then responds conversationally with the result. SnapLogic AgentCreator is purpose-built for such scenarios, enabling enterprises to create AI agents that can call pipelines and APIs autonomously. In our case, the agent will use a weather API through SnapLogic to get live data, meaning the agent's answers are not just based on static knowledge, but on real-time API calls. SnapLogic AgentCreator Architecture Overview We will focus on the AgentCreator pattern – a design that splits the agent’s logic into two cooperative pipelines: an Agent Driver and an Agent Worker. This pattern is orchestrated by SnapLogic’s Pipeline loop ( PipeLoop ) Snap, which allows iterative calls to a pipeline until a certain condition is met, in our case, until the conversation turn is complete or n number of iterations have been completed. Here’s how it works: Agent Driver pipeline: This orchestrator pipeline receives the incoming chat message and manages the overall conversation loop. It sends the user’s query ( plus any chat history messages available ) and the system prompt to the Agent Worker pipeline using the PipeLoop Snap, and keeps iterating until the LLM signals that it’s done responding or the number of iterations are reached. Agent Worker pipeline: This pipeline handles one iteration of LLM interaction. It presents the LLM with the conversation context and available tools, gets the LLM’s response ( which could be an answer or a function call request ), executes any required tool, and returns the result back to the Driver. The Worker is essentially where the "brain" of the agent lives – it decides if a tool call is needed and formats the answer. This architecture allows the agent to have multi-turn reasoning. For example, if the user asks for weather, the LLM might first respond with a function call to get data, the Worker executes that call, and then the LLM produces a final answer in a second iteration. The PipeLoop Snap in the Driver pipeline detects whether another iteration is needed ( if the last LLM output was a partial result or tool request ) and loops again, or stops if the answer is complete. Key components of the Weather Agent architecture: SnapLogic AgentCreator: The toolkit that makes this AI agent possible. It provides specialized Snaps for prompt handling, LLM integration ( OpenAI, Azure OpenAI, Amazon Bedrock, Google Gemini etc. ), and function-calling logic. SnapLogic AgentCreator enables designing AI agents with dynamic iteration and tool usage built in. LLM ( Generative AI model 😞 The LLM powering the agent's understanding and response generation. In our implementation, an LLM ( such as OpenAI GPT ) interprets the user’s request and decides when to call the available tools. SnapLogic’s Tool Calling Snap interfaces with the LLM’s API to get these decisions. Weather API: The external data source for live weather information. The agent uses a real API ( https://open-meteo.com/ ) to fetch current weather details for the requested location. Microsoft Teams & Azure Bot: This is the front-end interface where the user interacts with the bot, and the connector that sends messages between Teams and our SnapLogic pipelines. Setting up an OpenAI API account Because we are working with the gpt 4.1 mini API, we will need to configure an OpenAI account. This assumes you have already created an API key in your OpenAI dashboard. Navigate to Manager tab under your project folder location and click on the "+" button to create a new Account. Navigate to OpenAI LLM -> OpenAI API Key Account You can name it based on your needs or naming convention Copy and paste your API key from the OpenAI dashboard On the Agent Worker pipeline, open the "OpenAI Tool Calling" snap and apply the newly created account Save the pipeline. You have now successfully integrated the OpenAI API. Weather Agent pipelines in SnapLogic I've built a set of SnapLogic pipelines to implement the Weather Agent logic using AgentCreator. Each pipeline has a specific role in the overall chatbot workflow: WeatherAgent_AgentDriver: The orchestrator for the agent. It is triggered by incoming HTTP requests from the Azure Bot Service ( when a user sends a Teams message ). The AgentDriver parses the incoming message, sends a quick "typing…" indicator to the user ( to simulate the bot typing ), and then uses a PipeLoop Snap to invoke the AgentWorker pipeline. It supplies the user’s question, the system prompt and any prior context, and keeps iterating until the bot’s answer is complete. It also handles "deleting" chat history if the user writes a specific message like "CLEAR_CHAT" in the Teams agent conversation to refresh the conversation. WeatherAgent_AgentWorker: The tool-calling pipeline ( Agent Worker ) that interacts with the LLM. On each iteration, it takes the conversation messages ( system prompt, user query, and any accumulated dialogue history ) from the Driver. The flow of the Agent Worker for a Weather Agent: defines what tools ( functions ) the LLM is allowed to call – in this case, a location and weather lookup tools invokes the LLM via a Tool Calling Snap, passing it the current conversation and available function definitions processes the LLM’s response – if the LLM requests a function call ( "get weather for London" ), the pipeline routes that request to the appropriate tool pipeline once the tool returns data, the Worker formats the result using a Function Result Generator Snap and appends it to the conversation via a Message Appender Snap returns the updated conversation with any LLM answer or tool results back to the Driver. The AgentWorker essentially handles one round of "LLM thinking" WeatherAgent_GetLocation: A tool that the agent can use to convert a user’s input location ( city name, etc. ) into a standardized form or coordinates ( latitude and longitude ). It queries an open-meteo API to retrieve latitude and longitude data based on the given location. The system prompt instructs the agent that if the tool returns more than one match, ask the user which location they meant - keeping a human-in-the loop for such scenarios. For example, if the user requests weather for "Springfield", the agent first calls the GetLocation tool and if the tool responds with multiple locations, the agent will list them ( for example, Springfield, MA; Springfield, IL; Springfield, MO ) and ask the user to specify which location they meant before proceeding. Once the location is confirmed, the agent passes the coordinates to the GetWeather tool. WeatherAgent_GetWeather: The tool pipeline that actually fetches current weather data from an external API. This pipeline is invoked when the LLM decides it needs the weather info. It takes an input latitude and longitude and calls a weather API. In our case, I've used the open-meteo service, which returns a JSON containing weather details for a given location. The pipeline consists of an HTTP Client Snap ( configured to call the weather API endpoint with the location ) and a Mapper Snap to shape the API’s JSON response into the format expected by the Agent Worker pipeline. Once the data is retrieved ( temperature, conditions, etc. ), this pipeline’s output is fed back into the Agent Worker ( via the Function Result Generator ) so the LLM can use it to compose a user-friendly answer. MessageEndpoint_ChatHistory: This pipeline handles conversation history ( simple memory ) for each user or conversation. Because our agent may be used by multiple users ( and we want each user’s chat to be independent ), we maintain a user-specific chat history. In this example the pipeline uses the SLDB's storage to store the file but in a production environment the ChatHistory pipeline could use a database Snap store chat history, keyed by user or conversation ID. Each time a new message comes in, the AgentDriver will call this pipeline to fetch recent context ( so the bot can "remember" what was said before ). This ensures continuity in the conversation – for example, if the user follows up with "What about tomorrow?", the bot can refer to the previous question’s context stored in history. For simplicity, one could also maintain context in-memory during a single conversation session, but persisting it via this pipeline allows context across multiple sessions or a longer pause. SnapLogic introduced specialized Snaps for LLM function calling to coordinate this process. The Function Generator Snap defines the available tools that the LLM agent can use. The Tool Calling Snap sends the user’s query and function definitions to the LLM model and gets back either an answer or a function call request ( and any intermediate messages ). If a function call is requested, SnapLogic uses a Pipeline Execute or similar mechanism to run the corresponding pipeline. The Function Result Generator then formats the pipeline’s output into a form the LLM can understand. At the end, the Message Appender Snap adds the function result into the conversation history, so the LLM can take that into account in the next response. This chain of Snaps allows the agent to decide between answering directly or using a tool, all within a no-code pipeline. Sample interaction from user prompt to answer To make the above more concrete, let’s walk through the flow of a sample interaction step by step: User asks a question in Teams: "What's the weather in San Francisco right now?" This message is sent from Teams to the Azure Bot Service, which relays it as an HTTP POST to our SnapLogic AgentDriver pipeline’s endpoint ( the messaging endpoint URL we will configure in the second part ). AgentDriver pipeline receives the message: The WeatherAgent_AgentDriver captures the incoming JSON payload from Teams. This payload contains the user’s message text and metadata ( like user ID, conversation ID, etc. ). The pipeline will first respond immediately with a typing indicator to Teams. We configured a small branch in the pipeline to output a "typing" activity message back to the Bot service, so that Teams shows the bot is typing - implemented to mainly enhance UX while the user waits for an answer. Preparing the prompt and context: The AgentDriver then prepares the initial prompt for the LLM. Typically, we include a system prompt ( defining the bot’s role/behavior ) and the user prompt. If we have prior conversation history ( from MessageEndpoint_ChatHistory for this user ), we would also include recent messages to give context. All this is packaged into a messages array that will be sent to the LLM. AgentDriver invokes AgentWorker via PipeLoop: The Driver uses a PipeLoop Snap configured to call the WeatherAgent_AgentWorker pipeline. It passes the prepared message payload as input. The PipeLoop is set with a stop condition based on the LLM’s response status – it will loop until the LLM indicates the conversation turn is completed or the iteration limit has been reached ( for example, OpenAI returns a finish_reason of "stop" when it has a final answer, or "function_call" when it wants to call a function ). AgentWorker ( 1st iteration - tool decision 😞 In this first iteration, the Worker pipeline receives the messages ( system + user ). Inside the Worker: A Function Generator Snap provides the definition of the GetLocation and GetWeather tools, including their name, description, and parameters. This tells the LLM what the tool does and how to call it. The Tool Calling Snap now sends the conversation ( so far just the user question and system role ) along with the available tool definition to the LLM. The LLM evaluates the user’s request in the context of being a weather assistant. In our scenario, we expect it will decide it needs to use the tool to get the answer. Instead of replying with text, the LLM responds with a function call request. For example, the LLM might return a JSON like: The Tool Calling Snap outputs this structured decision. ( Under the hood, the Snap outputs it on a Tool Calls view when a function call is requested ). The pipeline splits into two parallel paths at this point: One path captures the LLM’s partial response ( which indicates a tool is being called ) and routes it into a Message Appender. This ensures that the conversation history now includes an assistant turn that is essentially a tool call. The other path takes the function call details and invokes the corresponding tool. In SnapLogic, we use a Pipeline Execute Snap to call the WeatherAgent_GetWeather pipeline. We pass the location ( "San Francisco" ) from the LLM’s request into that pipeline as input parameter ( careful, it is not a pipeline parameter ). WeatherAgent_GetWeather executes: This pipeline calls the external Weather API with the given location. It gets back a weather data ( say the API returns that it’s 18°C and sunny ). The SnapLogic pipeline returns this data to the AgentWorker pipeline. On the next iteration, the messages array would look something like below: AgentWorker ( function result return 😞 With the weather data now in hand, a Function Result Generator Snap in the Worker takes the result and packages it in the format the LLM expects for a function result. Essentially, it creates the content that will be injected into the conversation as the function’s return value. The Message Appender Snap then adds this result to the conversation history array as a new assistant message ( but marked in a way that the LLM knows it’s the function’s output ). Now the Worker’s first iteration ends, and it outputs the updated messages array ( which now contains: user’s question, assistant’s "thinking/confirmation" message, and the raw weather data from the tool ). AgentDriver ( loop decision 😞 The Driver pipeline receives the output of the Worker’s iteration. Since the last LLM action was a function call ( not a final answer ), the stop condition is not met. Thus, the PipeLoop triggers the next iteration, sending the updated conversation ( which now includes the weather info ) back into the AgentWorker for another round. AgentWorker ( 2nd iteration - final answer 😞 In this iteration, the Worker pipeline again calls the Tool Calling Snap, but now the messages array includes the results of the weather function. The LLM gets to see the weather data that was fetched. Typically, the LLM will now complete the task by formulating a human-friendly answer. For example, it might respond: "It’s currently 18°C and sunny in San Francisco." This time, the LLM’s answer is a normal completion with no further function calls needed. The Tool Calling Snap outputs the assistant’s answer text and a finish_reason indicating completion ( like "stop" ). The Worker appends this answer to the message history and outputs the final messages payload. AgentDriver ( completion 😞 The Driver receives the final output from the Worker’s second iteration. The PipeLoop Snap sees that the LLM signaled no more steps ( finish condition met ), so it stops looping. Now the AgentDriver takes the final assistant message ( the weather answer ) and sends it as the bot’s response back to Teams via the HTTP response. The pipeline will extract just the answer text to return to the user. User sees the answer in Teams: The user’s Teams chat now displays the Weather Agent's reply, for example: "It’s currently 18°C and sunny in San Francisco." The conversation context ( question and answer ) can be stored via the ChatHistory pipeline for future reference. From the user’s perspective, they asked a question and the bot answered naturally, with only a brief delay during which they saw the bot "typing" indicator. Throughout this interaction, the typing indicator that is implemented helps reassure the user that the agent is working on the request. The user-specific chat history ensures that if the user asks a follow-up like "How about tomorrow?", the agent could understand that "tomorrow" refers to the weather in San Francisco, continuing the context ( this would involve the LLM and pipelines using the stored history to know the city from prior turn ). This completes the first part, which was on how the SnapLogic pipelines and AgentCreator framework enable an AI-powered chatbot to use tools and deliver real-time info. We saw how the Agent Driver + Worker architecture ( with iterative PipeLoop execution ) allows interactions where the LLM can call SnapLogic pipelines as functions. The no-code SnapLogic approach made it possible to integrate an LLM without writing custom code – we simply configured Snaps and pipelines. We now have a working AI Agent that we can use in SnapLogic, however, we are still missing the chatbot experience. In the second part, we’ll shift to the integration with Microsoft Teams and Azure, to see how this pipeline is exposed as a bot endpoint and what steps are needed to deploy it in a real chat environment. AgentCreatorBuilding an AI Agent with SnapLogic AgentCreator using OpenAI and Microsoft Teams - Part 2
Integrating the AI Agent with Microsoft Teams via Azure Bot Service The first part covered the creation of our agent's architecture using SnapLogic pipelines and AgentCreator. Now, we focus on connecting that pipeline to Microsoft Teams so end users can chat with it. This involves creating and configuring the Azure Bot Service as a bridge between Teams and our SnapLogic pipelines. We will walk through the prerequisites and setup. Prerequisites for the Azure Bot Integration To integrate the SnapLogic agent with Teams, ensure you have the following prerequisites in place: SnapLogic AgentCreator and pipelines: A SnapLogic environment where AgentCreator is enabled. The Weather Agent pipelines can be used as a working example. You’ll also need to create the AgentDriver pipeline as a Triggered Task ( to obtain an endpoint URL accessible by the bot ). SnapLogic OAuth2 Account: An OAuth2 Account which will be used in an HTTP Client to send the assistant response back to the user. Also used for simulating "typing" indicator in Teams chat between tool usage. Microsoft 365 Tenant with Teams: Access to a Microsoft tenant where you have permission to register applications and upload custom Teams apps. You’ll need a Teams environment to test the bot ( this could be a corporate tenant or a developer tenant ). Azure Subscription: An Azure account with an active subscription to create resources ( specifically, an Azure Bot Service ). Also, ensure you have the Azure Bot Channels Registration or Azure Bot resource creation rights. Azure AD App Registration: Credentials for the bot. We will register an application in Azure Active Directory to represent our bot ( this provides a Client ID and Client Secret that will be used by the Bot Service to authenticate ). Azure Bot Service resource: We will create an Azure Bot which will tie together the app registration and our messaging endpoint, and allow adding Teams as a channel. Register an App in Azure AD for the Bot The first step is to register an Azure AD application that will identify our bot and provide authentication to Azure Bot Service and Teams. Create App registration: In the Azure Portal, navigate to Azure Active Directory > App Registrations and click "New registration". Give the app a name. For supported account types, you can choose "Accounts in this organizational directory only" ( Single tenant ) for simplicity, since this bot is intended for your organization’s Teams. You do not need to specify a Redirect URI for this scenario. Finalize registration: Click Register to create the app. Once created, you’ll see the Application ( Client ) ID – copy this ID, as we’ll need it later as the Bot ID and in the OAuth2 account. Create a client secret: In your new app’s overview, go to Certificates & secrets. Click "New client secret" to generate a secret key. Give it a description and a suitable expiration period. After saving, copy the Value of the client secret ( it will be a long string ). Save this secret somewhere secure now – you won’t be able to retrieve it again after you leave the page. We’ll provide this secret to the Bot Service so it can authenticate as this app and we will also use it in the OAuth2 account in SnapLogic. Gather Tenant ID: Since we chose a single-tenant app, we’ll also need the Azure AD tenant ID. You can find this in Overview of the app. Copy the tenant ID as well for later use. At this point, you should have: Client ID ( application ID ) for the bot and the OAuth2 account Client secret for the bot ( stored securely ) and the OAuth2 account Tenant ID of our Azure AD These will be used when setting up the Azure Bot Service so that it knows about this app registration. Create an OAuth2 account Now that we have the client id, client secret and tenant id all gathered from the app registration, we can create the OAuth2 account which will be used in an HTTP Client snap that will send the "typing" indicator as well as the response from the agent. Navigate to the "Manager" tab and locate your project folder where the agent pipelines are stored On the right side, click on the "+" icon to create a new account Choose "API Suite > OAuth2 Account" Populate the client id and client secret values from your app registration process Check the 'Send client data as Basic Auth header' and 'Header authenticated' settings Populate the authorization and token endpoints OAuth2 authorization endpoint: https://login.microsoftonline.com/<TENANT_ID>/oauth2/v2.0/authorize Oauth2 token endpoint: https://login.microsoftonline.com/<TENANT_ID>/oauth2/v2.0/token Change the "Grant type" to "client_credentials Add scope to both "Token endpoint config" and "Authorization endpoint config", the scope in our case is the following: https://api.botframework.com/.default Check "Auto-refresh token" Click "Authorize", if everything was set correctly on the previous step you should get redirected back to SnapLogic with a valid access token Example of an already configured OAuth2 account Create the Azure Bot Service and Connect to SnapLogic With the Azure AD app ready, we can create the actual bot resource that will connect to Teams and our SnapLogic endpoint: Add Azure Bot resource: In the Azure Portal, search for "Azure Bot" Service and select Azure Bot. Choose Create to make a new Bot resource. Configure Bot Settings: On the creation form, fill in: Bot handle: A unique name for your bot. Subscription and Resource Group: Select your Azure subscription and a resource group to contain the bot resource. Location: Pick a region. Pricing tier: Choose the Free tier if ( F0 ) – it’s more than sufficient for development and basic usage. Microsoft App ID: Here, reuse the existing App Registration we created. There should be an option to choose an existing app – provide the Client ID of the app registration. This links the bot resource to our AD app. App type: Select Single Tenant since our app registration is single-tenant. You might also need to provide the App secret ( Client Secret ) for the bot here during creation. Create the Bot: Click Review + create and then Create to provision the bot service. Azure will deploy the bot resource. Once completed, go to the resource’s page. Configure messaging endpoint: This is a crucial step – we must point the bot to our SnapLogic pipeline. In the Azure Bot resource settings, find the Settings menu and navigate to Configuration. Populate the field for Messaging endpoint ( the URL that the bot will call when a message is received ). Here, paste the Trigger URL of your WeatherAgent_AgentDriver pipeline. To get this URL: In SnapLogic, you would have already created the AgentDriver pipeline as a Triggered Task. That generates an endpoint URL: https://elastic.snaplogic.com/api/1/rest/slschedule/<org>/<proj>/WeatherAgent_AgentDriver Example endpoint with an appended authorization query param as bearer_token: https://elastic.snaplogic.com/api/1/rest/slschedule/myOrg/WeatherProject/WeatherAgent_AgentDriver?bearer_token=<bearer token> Enter the URL exactly as given by SnapLogic + Bearer token value Save the configuration. Now, when the Teams user messages the bot, Azure will send an HTTPS POST to this SnapLogic URL. Add Microsoft Teams channel: Still in the Azure Bot resource, go to Channels. Add a new channel and select Microsoft Teams. This step registers the bot with Teams so that Teams clients can use it. Now our bot service is set up with the SnapLogic pipeline as its backend. The AgentDriver pipeline is effectively the bot’s webhook. The Azure Bot resource handles authentication with Teams and will forward user messages to SnapLogic and relay SnapLogic’s responses back to Teams. Packaging the bot for Teams ( App manifest ) At this stage, the bot exists in Azure, but to use it in Teams we need to package it as a Teams app, especially if we want to share it within the organization. This involves creating a Teams app manifest and icons, then uploading it to Teams. Prepare the Teams app manifest: The manifest is a JSON file describing your Teams app ( the bot ). Microsoft provides a schema for this but you can download the manifest file from this example, make sure you replace the <APP ID> placeholders within it. The manifest file consists of: App ID: Use the Bot’s App ID ( Client ID of the registered app ) App name, description: The name of Teams app, example "SnapLogic Agent". Icons: Prepare two icon images for the bot – typically a color icon ( 192x192 PNG ) and an outline icon ( 32x32 PNG ). These will be used as the agent's avatar and in the Teams app catalog. The manifest may also include information like developer info, version number, etc. If using the Teams Developer Portal, it can guide you through filling these fields and will handle the JSON for you. Just ensure the Bot ID and scopes are correctly set. Combine manifest and icons: Once your manifest file and icons are ready, put all three into a .zip file. For example, a zip containing: manifest.json icon-color.png ( 192x192 ) icon-outline.png ( 32x32 ) Make sure the JSON inside the zip references the icon file names exactly as they are. Upload the app to Teams: In Microsoft Teams, go to Apps > Manage your apps > Upload a custom app. Upload the zip file. Teams should recognize it as a new app. When added, it essentially registers the bot ID with the Teams client. Test in Teams: Open a chat with your Weather Agent in Teams ( it should appear with the name and icon you provided ). Type a message, like "Hi" or a weather question: "What's the weather in New York?" The message will go out to Azure, which will call SnapLogic’s endpoint. The SnapLogic pipelines will run through the logic ( as described in in the first part ) and Azure will return the bot’s reply to Teams. You should see the bot’s answer appear in the chat. If you see the bot typing indicator first and then the answer, everything is working as expected! Initial message and a response from the agent Typing indicator as showcased during agent execution Agent response after using the available tools Now the Weather Agent is fully functional within Teams. It’s essentially an AI-powered chat interface to a live weather API, all orchestrated by SnapLogic in the background. Benefits of SnapLogic and Teams for Conversational Agentic Interfaces Integrating SnapLogic AgentCreator with Microsoft Teams via Azure Bot Service has several benefits: Fast prototyping: You can go from idea to a working bot in a very short time. There’s no need to write custom bot code or host a web service – SnapLogic pipelines become your bot logic. In our example, building a weather query bot is as simple as wiring up a few Snaps and APIs. This accelerates development and allows quick iteration. Business users or integration developers can prototype new AI agents rapidly, responding to evolving needs without a heavy software development cycle. No-code integration and simplicity: SnapLogic provides out-of-the-box connectors to hundreds of systems and services. By using SnapLogic as the engine, your bot can tap into any of these with minimal effort. Want a bot that not only gives weather but also looks up flight data or CRM info? It’s just another pipeline. The AgentCreator framework handles the AI part, while the SnapLogic platform handles the integration part ( connecting to external APIs and data sources ). This synergy makes it simple to create powerful bots that perform real actions – far beyond what an LLM alone could do. And it’s all done with low/no-code configuration. Enhanced user experience: Delivering automation through a conversational interface in Teams meets users where they already collaborate. There’s no new app to learn – users simply chat with a bot as if they’re chatting with a colleague. Reusability: The modular design of the pipelines in the weather agent can be a template for other agents by swapping out the tools and prompts. The integration pattern remains the same. This showcases the reusability of the AgentCreator approach across various use cases. Conclusion By combining SnapLogic’s generative AI integration capabilities with Microsoft’s bot framework and Teams, we created a powerful AI Agent without writing any code at all. We used SnapLogic AgentCreator snaps to handle the AI reasoning and tool calling, and used Azure Bot Service to connect that logic to a Microsoft Teams. The real win is how quickly and easily this was achieved. In a matter of days or even hours, an enterprise can prototype a conversational AI agent that ties into live data and services. The speed of development, combined with the secure and integration into everyday platforms like Teams, delivers real business value. In summary, SnapLogic and Teams enables a new class of enterprise applications: ones that talk to you, using AI to bridge human requests to automated actions. The Weather Agent is a simple example, but it highlights how fast prototyping, integration simplicity, and enhanced user experience come together. I encourage you to try building your own SnapLogic Agent – whether it’s for weather, workflows, or anything else – and unleash the power of conversational AI in your organization. Happy integrating, and don’t forget your umbrella if the Weather Agent says rain is on the way! AgentCreatorRecipes for Success with SnapLogic’s GenAI App Builder: From Integration to Automation
For this episode of the Enterprise Alchemists podcast, Guy and Dominic invited Aaron Kesler and Roger Sramkoski to join them to discuss why SnapLogic's GenAI App Builder is the key to success with AI projects. Aaron is the Senior Product Manager for all things AI at SnapLogic, and Roger is a Senior Technical Product Marketing Manager focused on AI. We kept things concrete, discussing real-world results that early adopters have already been able to deliver by using SnapLogic's integration capabilities to power their new AI-driven experiences.
Multi Pipeline Function Generator - Simplifies Agent Worker Pipeline
This article introduces a new Snap called the “Multi Pipeline Function Generator”. The Multi Pipeline Function Generator is designed to take existing Pipelines in your SnapLogic Project and turn their configurations into function definitions for LLM-based tool calling. It achieves the following: It replaces the existing chain of function generators, therefore reduces the length of the worker pipeline. Combined with our updates to the tool calling snaps, this snap allows multiple tool calling branches to be merged into a single branch, simplifying the pipeline structure. With it, users can directly select the desired pipeline to be used as a tool from a dropdown menu. The snap will automatically retrieve the tool name, purpose, and parameters from the pipeline properties to generate a function definition in the required format. Problem Statement Currently, the complexity of the agent worker pipeline increases linearly with the number of tools it has. The image below shows a worker pipeline with three tools. It requires three function generators and has three tool calling branches to execute different tools. This becomes problematic when the number of tools is large, as the pipeline becomes very long both horizontally and vertically. Current Agent Worker Pipeline With Three Tools Solution Overview One Multi Pipeline Function Generator snap can replace multiple function generators (as long as the tool is a pipeline; it's not applicable if the tool is of another type, such as OpenAPI or APIM service). New Agent Worker Pipeline Using “Multi Pipeline Function Generator” Additionally, for each outputted tool definition, it includes the corresponding pipeline's path. This allows downstream components (the Pipeline Execute snap) to directly call the respective tool pipeline with the path, as shown below. The Multi Pipeline Function Generator snap allows users to select multiple tool pipelines at once through dropdown menus. It reads the necessary data for generating function definition from the pipeline properties. Of course, this requires that the data has been set up in the pipeline properties beforehand (will be explained later). The image below shows the settings for this snap. Snap Settings How to Use the Snap To use this snap, you need to: Fill in the necessary information for generating the function definition in the properties of your tool pipeline. The pipeline's name will become the function name The information under 'info -> purpose' will become the function description. Each key in your OpenAPI specification will be treated as a parameter, so you will ALSO need to add the expected input parameters to the list of pipeline parameters. Please note that in the current design, the pipeline parameters specified here are solely used for generating the function definition. When utilizing parameters within the pipeline, you do not need to retrieve their values using pipeline parameters. Instead, you can directly access the argument values from the input document, as determined by the model based on the function definition. Then, you can select this pipeline as a tool from the dropdown menu in the Multi Pipeline Function Generator snap. In the second output of the tool calling snap, we only need to keep one branch. In the pipeline execute snap, we can directly use the expression $sl_tool_metadata.path to dynamically retrieve the path of the tool pipeline being called. See image below. Below is an example of the pipeline properties for the tool 'CRM_insight' for your reference. Below is the settings page of the original function generator snap for comparison. As you can see, the information required is the same. The difference is that now we directly fill this information into the pipeline's properties. Step 3 - reduce the number of branches More Design Details The tool calling snap has also been updated to support $sl_tool_metadata.path , since the model's initial response doesn't include the pipeline path which is needed. After the tool calling snap receives the tools the model needs to call, it adds the sl_tool_metadata containing the pipeline path to the model's response and outputs it to the snap's second output view. This allows us to use it in the pipeline execute snap later. This feature is supported for tool calling with Amazon Bedrock, OpenAI, Azure OpenAI, and Google GenAI snap packs. The pipeline path can accept either a string or a list as input. By turning on the 'Aggregate input' mode, multiple input documents can be combined into a single function definition document for output, similar to that of a gate snap. This can be useful in scenarios like this: you use a SnapLogic list snap to enumerate all pipelines within a project, then use a filter snap to select the desired tool pipelines, and finally use the multi pipeline function generator to convert this series of pipelines into function definitions. Example Pipelines Download here. Conclusion In summary, the Multi Pipeline Function Generator snap streamlines the creation of function definitions for pipeline as tool in agent worker pipelines. This significantly reduces pipeline length in scenarios with numerous tools, and by associating pipeline information directly with the pipeline, it enhances overall manageability. Furthermore, its applicability extends across various providers.
Best Practices for Adopting AI Solutions in the Enterprise with SnapLogic AgentCreator
Best Practices for Adopting AI Solutions in the Enterprise with SnapLogic AgentCreator Version: 1.2 Authors: Dominic Wellington, Guy Murphy, Pat Traynor, Bash Badawi, Ram Bysani, David Dellsperger, Aaron Kesler Introduction: AI in the Modern Enterprise AI is fast becoming a cornerstone of modern enterprises, transforming how businesses operate, make decisions, and interact with customers. Its capabilities, such as automation, predictive analytics, and natural language processing, allow companies to streamline processes, gain deeper insights from data, and enhance customer experiences. From optimizing supply chains to personalizing marketing strategies, AI is enabling enterprises to innovate, drive efficiency, and be competitive in an increasingly data-driven world. As AI continues to evolve, its role in shaping business strategy and operations will only grow. Precisely because of its novelty and importance, leaders will need to think carefully about various aspects of how these powerful new capabilities can be deployed in a manner that is compliant with existing legislation and regulation, and how best to integrate them with existing systems and processes. There are no generally-accepted best practices in this field yet due to its novelty, but there are lessons that we can learn from past waves of technological change and adoption. In this document we set out some suggestions for how to think about these topics in order to ensure a positive outcome. Data Data is the lifeblood of IT — arguably the reason for the field’s entire existence — but its importance is only magnified when it comes to AI. Securing access to data is a requirement for an AI project to get off the ground in the first place, but managing that access over time, especially as both the data and the policies that apply to it change and evolve over time. Data Security and Management Data security has always been a complex issue for IT organizations. When considered from the perspective of AI adoption, two main areas need to be considered: external and internal usage. Externally-hosted Large Language Models (LLMs) offer powerful and rapidly-evolving capabilities, but also have inherent risks as they are operated by third parties. The second area of focus is how and what internal data should be used with AI models, whether self-managed or externally operated. External Security Organizations have reasonable concerns about their proprietary, regulated, or otherwise sensitive information “leaking” beyond the organizational boundaries. For this reason, simply sending internal information to a public LLM without any controls in place is not considered a viable solution. An approach to this problem that was previously considered promising was to use a technique called Retrieval Augmented Generation, or RAG. In this approach, rather than passing user queries directly to an LLM for answers, a specialized data store is deployed, called a vector database. When a user query is received, the vector data store is consulted first to identify relevant chunks of information with which to answer the query, and only after this step is the LLM used to provide the conversational response back to the user. However, while RAG does limit the potential for information leakage, it does not reduce it to zero. The vector database can be operated according to the organization’s own risk profile: fully in-house, as a private cloud instance, or leveraging a shared platform, depending on the information it contains and the policies or regulations that apply to that information. However, a chunk of information will be sent from the vector store to the LLM to answer each query, and over time, this process can be expected to expose a substantial part of the knowledge base to the LLM. It is also important to be aware that the chunking process itself still uses a LLM. More security-sensitive organizations or those operating in regulated industries may choose to leverage a more restricted deployment model for the LLM as well, much as discussed for the vector database itself, in order to avoid this leakage. However, it is worth noting that while an “open-source” language model can be prevented from contributing training data back to its developers, its own pre-existing training data may still leak out into the answers. The ultimate risk here is of “model poisoning” from open-source models. That is, injection of data from outside the user’s domain which may lead to inconsistent or undesirable responses. One example of this phenomenon is “context collapse”, which may occur in the case of overloaded acronyms, where the same acronym can represent vastly different concepts in different domains. A generalist model may mis-understand or mis-represent the acronym — or worse, may do so inconsistently. The only way to be entirely certain of data security and hygiene is to train the model from scratch — an undertaking that, due to its cost in both time and resources, is practical only for the largest organisations, and is anyway required only for the most sensitive data sets. A halfway house that is suitable for organisations that have concerns in this domain, but not to the point of being willing to engineer everything themselves from the ground up, is fine-tuning. In this approach, a pre-trained model is further trained on a specific data set. This is a form of transfer learning where a pre-trained model trained on a large dataset is adapted to work for a specific task. The dataset required for this sort of fine-tuning is very small compared to the dataset required for full model training, bringing this approach within reach of far more organisations. Internal Data Access Controls The data that is consumed by the AI model also needs to be secured inside the organization, ensuring that access controls on that data follow the data through the system at all levels. It is all too easy to focus on ingesting the data and forget about the metadata, such as role-based access controls. Instead, these controls should be maintained throughout the AI-enabled system. Any role-based access controls (RBAC) that are placed on the input data should also be reflected in the output data. Agentic approaches are useful here, as they give the opportunity to enforce such controls at various points. The baseline should be that, if a user ought not be able to access certain information through traditional means such as database queries or direct filesystem access, they also must not be able to access it by querying an AI overlay over those systems — and vice-versa, of course. Prompt Logging and Observability An emerging area of concern is the security of prompts used with AI models. Especially when using public or unmodified open-source models, the primary input to the models is the prompt that is passed to them. Even minor changes to that prompt can cause major differences in what is returned by the model. For this reason, baseline best practice is to ensure that prompts are backed up and versioned, just as would be done for more traditional program code. In addition, both prompts and their corresponding responses should be logged in order to be able to identify and troubleshoot issues such as performance changes or impact to pricing models of public LLMs. Some more detailed suggestions are available here. Prompts should also be secured against unauthorized modification, or “prompt injection”. Similarly to the analogous “SQL injection”, attackers may attempt to modify or replace the prompt before it is passed to the AI model, in order to produce outputs that are different from those expected and desired by users and operators of the system. The potential for damage increases further in the case of agentic systems that may chain multiple model prompts together, and potentially even take actions in response to those prompts. Again, logging for both in-the-moment observability and later audit is important here, including the actual final prompt that was sent to the model, especially when that has been assembled across multiple steps. These logs are useful for troubleshooting, but may also be formally required for demonstrating compliance with regulation or legislation. Example Prompt Injection Scenarios Direct Injection An attacker injects a prompt into a customer support chatbot, instructing it to ignore previous guidelines, query private data stores, and send emails, leading to unauthorized access and privilege escalation. Indirect Injection A user employs an LLM to summarize a webpage containing hidden instructions that cause the LLM to insert an image linking to a URL, leading to exfiltration of the private conversation. Unintentional Injection A company includes an instruction in a job description to identify AI-generated applications. An applicant, unaware of this instruction, uses an LLM to optimize their resume, inadvertently triggering the AI detection. Intentional Model Influence An attacker modifies a document in a repository used by a Retrieval-Augmented Generation (RAG) application. When a user’s query returns the modified content, the malicious instructions alter the LLM’s output, generating misleading results. Code Injection An attacker exploits a vulnerability in an LLM-powered email assistant to inject malicious commands, allowing access to sensitive information and manipulation of email content. Payload Splitting An attacker uploads a resume with split malicious prompts. When an LLM is used to evaluate the candidate, the combined prompts manipulate the model’s response, resulting in a positive recommendation regardless of the resume’s actual contents. Multimodal Injection An attacker embeds a malicious prompt within an image that accompanies benign text. When a multimodal AI processes the image and text concurrently, the hidden prompt alters the model’s behavior, potentially leading to unauthorized actions or disclosure of sensitive information. Adversarial Suffix An attacker appends a seemingly meaningless string of characters to a prompt, which influences the LLM’s output in a malicious way, bypassing safety measures. Multilingual/Obfuscated Attack An attacker uses multiple languages or encodes malicious instructions (e.g., using Base64 or emojis) to evade filters and manipulate the LLM’s behavior. reference: https://genai.owasp.org/llmrisk/llm01-prompt-injection/ As these examples show, there are many patterns and return sets from LLMs that will need to be managed and observed, comparing prompts, responses, and data sets with certified sets and expected structures. Hopefully over time and as commercial LLMs mature, many of these issues will be managed by the LLMs themselves, but today these concerns will have to be part of the enterprise’s own governance framework for AI adoption. Data Ownership and Observability Much of the value of most Generative AI (GenAI) applications is based on the quantity, freshness and reliability of the source data that is provided. An otherwise fully-functional GenAI tool that provides responses based on incomplete or out-of-date data will not be useful or valuable to its users. The first question is simply how to gain access to useful source data, and to maintain that access in the future. This work spans both technical and policy aspects. SnapLogic of course makes technical connectivity easy, but there may still be questions of ownership and compliance, not to mention identifying where necessary data even resides. Beyond the initial setup of the AI-enabled system, it will be important to maintain ongoing access to up-to-date data. For instance, if a RAG approach is used, the vector data store will need to be refreshed periodically from the transactional data platform. The frequency of such updates will vary between use cases, depending on the nature of the data and its natural rate of change. For instance, a list of frequently asked questions, or FAQs, can be updated whenever a new entry is added to the list. Meanwhile, a data set that is updated in real time, such as airline operations, will need much more frequent synchronization if it is to remain useful. Recommendations Data is key to the success of AI-enabled systems – and not just one-time access to a dataset that is a point-in-time snapshot, but ongoing access to real-time data. Fortunately, these are not new concerns, and existing tools and techniques can be applied readily to securing and managing that flow of data. In fact, the prominence and urgency of AI projects can even facilitate the broad deployment of such tools and techniques, where they had previously been relegated to specialised domains of data and analytics. It is important to note that as the SnapLogic platform facilitates connectivity and movement of data, none of the patterns of such movement are used to enrich the learnings of a LLM. In other words, pipelines act to transport encrypted data from source to destination without any discernment of the actual payload. No payload data and no business logic governing the movement of data are ever gleaned from such movement or used to train any models. In fact, the SnapLogic platform can be used to enhance data security at source and destination as highlighted above, adding guardrails to an AI system to enforce policies against publication of sensitive or otherwise restricted data. In general it is recommended for domain experts, technical practitioners, and other stakeholders to work together and analyze each proposed use case for AI, avoiding both reflexive refusals and blind enthusiasm, focusing instead on business benefit and how to achieve that in a specific regulatory or policy context. Auditability and Data Lineage The ability to audit the output of AI models is a critical requirement, whether for routine debugging, or in response to incoming regulation (e.g. the EU AI Act) that may require auditability for the deployment of AI technology in certain sectors or for particular use cases. For instance, use of AI models for decision support in legal cases or regulated industries, especially concerning health and welfare, may be subject to legal challenges, leading to requests to audit particular responses that were generated by the model. Commercial and legal concerns may also apply when it comes to use cases that may impinge on IP protection law. Complete forensic auditability of the sort that is provided by traditional software is not possible for LLMs, due to their non-deterministic nature. For this reason, deterministic systems may still be preferable in certain highly-regulated spaces, purely to satisfy this demand. However, a weaker definition of auditability is becoming accepted when it comes to LLMs, where both inputs and outputs are preserved, and the model is required to provide the source information used to generate that output. The source data is considered important both to evaluate the factual correctness of the answer, and also to identify any bias which may make its way into the model from its source data. These factors make auditability and data lineage critical part of the overall AI Strategy, which will have to be applied at various different stages of the solution lifecycle; Model creation and training - This aspect relates to how the model was created, and whether the data sets used to train the model have a risk of either skewing over time, or exposing proprietary information used during model development. Model selection - The precise version of AI model that was used to generate a response will need to be tracked, as even different versions of the same model may produce different responses to the same prompt. For this reason it is important to document the moment of any change in order to be able to track and debug any drift in response or behaviour. For external third-party AI services, these models may need to be tested and profiled as part of both an initial selection process and ongoing validation The reality is that there are is no single AI that is best for all use cases. Experience from real-world deployment of actual AI projects shows that some models are noticeably better for some functions than others, as well as having sometimes radically different cost profiles. Thiese factors means that most probably several different AI models will be used across an enterprise, and even (as agents) to satisfy a single use case. Prompt engineering - Unlike in traditional software development, by its very nature, prompt engineering includes the data sets and structures in the development cycle in a way that traditional functional coding practices do not. The models’ responses are less predictable, so understanding how the data will be processed is an integral part of the prompt engineering lifecycle. To understand how and why a set of prompts are put into production will be driven by both the desired functionality and the data that will be provided, in order to be able to review these inputs if issues arise in the production environment. Prompt evaluation in production - All critical systems today should have robust logging and auditing processes. In reality however many enterprises rarely achieve universal deployment, and coverage is often inconsistent. Due to the nature of AI systems, and notably LLMs, there will be a critical need to audit the data inputs and outputs. The model is effectively a black box that is not available for operators to reconstruct exactly why a given response was provided. This issue is especially critical when multiple AI models, agents, and systems are chained together or networked within a wider process. Logging the precise inputs that are sent to the model will be key for all these purposes. For custom-trained models, these requirements may also extend to the training data — although this case is presumed to remain relatively rare for the foreseeable future, given the prohibitive costs of performing such training. Where more common approaches (RAG, fine-training) are used that do not require an entire model to be trained from scratch, the audit would naturally focus on the inputs to the model and how those are managed. In both these cases, good information hygiene should be maintained, including preservation of historical data for point-in-time auditability. Backing up the data inputs is necessary but not sufficient: after all, a different LLM (or a subsequent version of the same LLM) may provide different responses based on the same prompt and data set. Therefore, if a self-trained LLM is employed, that model should also be backed up in the same way as the data that feeds it. If a public LLM is used, rigorous documentation should be maintained identifying any changes or version upgrades to the external model. All of this work is in addition to the tracking of the prompts and data inputs themselves, as described previously. All of these backups will in turn need to be preserved according to whatever evidentiary concerns are expected to apply. In the case of simple technical audits to ensure continuous improvements and avoid downward pressure on the quality of responses provided, organizations can make their own determination on the level of detail, the width of the time window to be preserved, and the granularity of the data. In more highly regulated scenarios, some or all of these elements may be mandated by outside parties. In those situations, the recommendation would also generally be to specify the backup policy defensively, to avoid any negative impacts in the case of future challenges. Development and Architecture Best Practices While AI systems have notable differences from earlier systems, they are still founded in large part on pre-existing components and techniques, and many existing best practices will still apply, if suitably modified and updated. CI/CD Continuous Integration and Continuous Deployment is of course not specific to Generative AI. However, as GenAI projects move from demo to production, and then evolve over subsequent releases, it becomes necessary to consider them as part of that process. Many components of a GenAI application are stateful, and the relationship between them can also be complex. A roll-back of a vector data store used to support a RAG application may have unforeseen effects if the LLM powering that RAG application remains at a different point of the configuration timeline. Therefore the different components of an AI-enabled system should be considered as tightly coupled for development purposes, as otherwise the GenAI component risks never becoming a fully-fledged part of the wider application environment. In particular, all of the traditional CI/CD concepts should apply also to the GenAI component: Continuous Development Continuous Testing Continuous Integration Continuous Deployment Continuous Monitoring Ensuring the inclusion of development teams in the process is unlikely to be a problem, as the field of AI is still evolving at breakneck pace. However, some of the later stages of an application’s lifecycle are often not part of the worldview of the developers of early demo AI applications, and so may be overlooked in the initial phases of productization of GenAI functionality. All of these phases also have specific aspects that should be considered when it comes to their application to GenAI, so they cannot simply be integrated into existing processes, systems, or modes of thought. New aspects of development and DevOps are needed to support, notable prompt engineering will have to be treated as code artifacts, but will also have to be associated with prompts such as model version, test data sets and samples of return data so that consistent functional management of the combined set of capabilities can be understood and tracked over time. QA and Testing Quality Assurance (QA) and testing strategies for AI projects, particularly GenAI, must address challenges that differ significantly from traditional IT projects. Unlike traditional systems where output is deterministic and follows predefined rules, GenAI systems are probabilistic and rely on complex models trained on vast datasets. A robust QA strategy for GenAI must incorporate dynamic testing of outputs for quality, coherence, and appropriateness across a variety of scenarios. This involves employing both automated testing frameworks and human evaluators to assess the AI's ability to understand prompts and generate contextually accurate responses, while also mitigating risks such as bias, misinformation, or harmful outputs. A GenAI testing framework should include unique approaches like model evaluation using synthetic and real-world data, stress testing for edge cases, and adversarial testing to uncover vulnerabilities such as the attack scenarios listed above. Frameworks such as CI/CD are essential but need to be adapted to accommodate iterative model training and retraining processes. Tools like Explainable AI (XAI) help provide transparency into model decisions, aiding in debugging and improving user trust. Additionally, feedback loops from production environments become vital in fine-tuning the model, enabling ongoing improvement based on real-world performance metrics rather than static, pre-defined test cases. However, depending on the use case and the data provided, such fine-tuning based on user behaviour may itself be sensitive and need to be managed with care. The QA process for GenAI also emphasizes ethical considerations and regulatory compliance more prominently than traditional IT projects. Testing needs to go beyond technical correctness to assess social impact, ensuring that the system avoids perpetuating harmful bias or misinformation. Continuous monitoring after deployment is crucial, as model performance can degrade over time due to shifting data distributions. This contrasts with traditional IT projects, where testing is often a finite phase before deployment. In GenAI, QA is an evolving, lifecycle-long endeavor requiring multidisciplinary collaboration among data scientists, ethicists, domain experts, and software engineers to address the complex, dynamic nature of generative models. Grounding, as an example, is a technique that can be used to help produce model responses that are more trustworthy, helpful, and factual. Grounding generative AI model responses means connecting them to verifiable sources of information. To implement groundin, usually means retrieving relevant source data. The recommended best practice is to use the retrieval-augmented generation (RAG) technique. Other test concepts include; Human-in-the-Loop Testing: Involves human evaluators judging the quality, relevance, and appropriateness of the model's outputs. Source data accuracy with details and data. Adversarial Testing: Actively trying to "break" the model by feeding it carefully crafted inputs designed to expose weaknesses and vulnerabilities Deployment This aspect might superficially be considered among the easiest to cover, but that may well not be the case. Most CI/CD pipelines are heavily automated; can the GenAI aspects be integrated easily into that flow? Some of the processes involved have long durations, e.g. chunking a new batch of information; can they be executed as part of a deployment, or do they need to be pre-staged so that the result can simply be copied into the production environment during a wider deployment action? Monitoring Ongoing monitoring of the performance of the system will also need to be considered. For some metrics, such as query performance or resource utilization, it is simply a question of ensuring that coverage also spans to the new GenAI experience. Other new metrics may also be required that are specific to GenAI, such as users’ satisfaction with the results they receive. Any sudden change in that metric, especially if correlated with a previous deployment of a change to the GenAI components, is grounds for investigation. While extensive best practices exist for the identification of technical metrics to monitor, these new metrics are still very much emergent, and each organization should consider carefully what information is required — or is likely to be required in the event of a future investigation or incident response scenario. Integration of AI with other systems and applications AI strategies are already moving beyond pure analytic or chatbot use case, as the agentic trend continues to develop. These services, whether home grown or hosted by third parties, will need to interface with other IT systems, most notably business processes, and this integration will need to be well considered to be successful. Today LLMs are producing return sets in seconds, and though the models are getting quicker, there is a trend to trade time for greater resilience of quality. How this trade-off is integrated into high performance business systems that operate many orders of magnitude faster will need to be considered and managed with care. Finally, as stated throughout this paper, AI’s non deterministic nature will mandate a focus on compensating patterns across the blend of AI and process systems. Recommendation While it is true that the specifics of AI-enabled systems differ from previous application architectures, general themes should still be carried over, whether by analogy, applying the spirit of the techniques to a new domain, or by ensuring that the more traditional infrastructural components of the AI application are managed with the same rigour as they would be in other contexts. Service / Tool Catalog The shift from content and chatbot experiences to agentic approaches imply a new fundamental architectural consideration of which functions and services should be accessible for use by the model. In the public domain there are simple patterns and models will mainly be operating with other public services and sources — but in the enterprise context, the environment will be more complex. Some examples of questions that a mature enterprise will need to address to maximize the potential of an agentic capability ; What are the “right” services? Large enterprises have hundreds, if not thousands, of services today, all of which are (or should be)managed according to what their business context is. A service management catalog will be key to manage many of these issues,as this will give a consistent point of entry to the service plane. Here again, pre-existing API management capabilities can ensure that the right access and control policies can be applied to support the adoption of composable AI-enabled applications and agents. When it comes to security profiling of a consuming LLM, the requester of the LLM service will have a certain level of access based on a combination of user role and security policy that is enforced. The model will have to pass on this to core systems at run time so that there are no internal data breaches. When it comes to agentic systems, new questions arise, beyond the simpler ones that apply to generative or conversational applications. For instance, should an agent be able to change a record? How much change should be allowed and how will this be tracked? Regulatory Compliance While the field of GenAI-enabled applications is still extremely new, best practices are beginning to emerge, such as those provided by the Open Web Application Security Project (OWASP). These cybersecurity recommendations are of course not guaranteed to cover any particular emerging regulation, but should be considered a good baseline which is almost certain to give a solid foundation from which to work to achieve compliance with national or sector-specific regulation and legislation as it is formalised. In general, it is recommended to ensure that any existing controls on systems and data sets, including RBAC and audit logs, are extended to new GenAI systems as well. Any changes to the new components — model version upgrades, changes to prompts, updates to training data sets, and more — will need to be documented and tracked with the same rigour as established approaches would mandate for traditional infrastructure changes. The points made previously about observability and auditability all contribute to achieving that foundational level of best-practice compliance. It is worth reiterating here that full coverage is expected to be an important difference between GenAI and previous domains. Compliance is likely to go far beyond the technical systems and their configurations, which were previously sufficient, and to require tracking of final prompts as supplied to models, including user input and runtime data. Conclusion Planning and managing the deployment and adoption of novel AI-enabled applications will require new policies and expertise to be developed. New regulation is already being created in various jurisdictions to apply to this new domain, and more is sure to be added in coming months and years. However, much as AI systems require access to existing data and integration with existing systems to deliver value at scale, existing policies, experience, and best practices can be leveraged to ensure success. For this reason, it is important to treat AI as an integral part of strategy, and not its own isolated domain, or worse, delegated to individual groups or departments without central IT oversight or support. By engaging proactively with users’ needs and business cases, IT leaders will have a much better chance of achieving measurable success and true competitive advantage with these new technologies — and avoiding the potential downsides: legal consequences of non-compliance, embarrassing public failures of the system, or simply incorrect responses being generated and acted upon by employees or customers.
